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| About Lance Westerhoff (Quantum Bio) |
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Lance Westerhoff earned his Ph.D. in Chemistry, in Dr. Kenneth M. Merz Jr.'s research group at The Pennsylvania State University where he focused on the application of macromolecular, linear-scaling quantum mechanics and database design to problems in proteins-ligand complexes. In 2001, while still a graduate student, Lance worked with Dr. Merz to form QuantumBio Inc. in order to commercialize the linear-scaling, quantum mechanics software under active development in the academic laboratory. Soon after graduation Lance took over as General Manager of QuantumBio and he has remained there ever since and has successfully funded various research projects aimed at validating and commercializing quantum-based methods to study proteins and their ligands. Today he leads a team of computational chemists and software developers employed under three different NIH SBIR/STTR projects bent on fleshing out these applications including quantum-based protein-ligand scoring and interaction profiling, Xray refinement, and NMR-based pose scoring.
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Application of Quantum Mechanical Pairwise Energy Decomposition to Structure-based Drug Design
Lance Westerhoff, QuantumBio
The current state of the art of in silico drug discovery relies almost exclusively on molecular mechanics force fields and empirical potentials. It is well known that while these approaches are excellent for certain applications, they have thus far proven less then satisfactory for a thorough understanding of the interactions of enzyme-inhibitor systems. To address these issues, our linear scaling, quantum mechanics (QM) algorithm is being applied to in silico drug discovery problems to characterize pairwise energy decomposition (QM-PWD) between a set of targets and a population of inhibitors. Thus the QM-PWD method and associated SE-COMBINE application has been successfully developed and validated against two conventional methods (COMBINE and MM-PB(GB)SA) by comparing the methods’ abilities in elucidating binding affinities of a series of trypsin inhibitors. In order to measure the statistical robustness of the various methods, a partial least squares (PLS) analysis was performed for the results from SE-COMBINE and COMBINE calculations. The present study not only shows that the QM-PWD/SE-COMBINE method possesses a much greater flexibility in its use of atom-by-atom, pairwise energy decomposition and ligand fragmentation compared to other methods, but QM-PWD/SE-COMBINE is also shown to yield results that are significant improvements over those generated by conventional methods. Further, unlike these conventional methods, SE-COMBINE provides both QM and molecular mechanics (MM) energy terms, from which many scoring functions can be constructed on the fly and with little or no additional CPU cost. This ability allows QM-PWD/SE-COMBINE to be applied to a large breadth of receptor-ligand systems that may emphasize or require different energy terms. In the trypsin-ligand system, twelve different scoring functions with several combinations of various energy terms and ligand-fragmentation schemes were developed and validated, and robust PLS models were derived. The QM energy terms, such as QM-PWD in vacuum and solvent, have been demonstrated to be important to describe activation variation in trypsin-ligand system, and the scoring functions including mainly the MM energy terms yield less descriptive prediction sets. Thus, it has been shown that the QM energy terms are essential in accurate characterization of the trypsin-ligand system.
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